Wout Megchelenbrink and Richard Notebaart (photo up), Dept. of Internal Medicine and Centre for Systems Biology and Bioenergetics (CSBB), theme Infectious diseases and global health recently published a PNAS article describing a computational systems biology approach to predict tumor size and cancer patient survival.
The approach predicts Synthetic Dosage Lethality (SDL), which denotes a genetic interaction whereby an underexpression of gene A combined with an overexpression of gene B kills the cell. Many overexpressed oncogenes driving tumor growth are difficult to target directly, but inhibiting their SDL partners may selectively kill cancer cells. The authors present the first network-level modeling approach that is able to predict metabolic SDLs. As expected, they find that the predicted SDLs are less frequently active in tumors to avoid lethality. Cancer tumors with more and stronger SDLs have smaller tumor size and lead to increased patient survival. Beyond facilitating the development of novel anticancer therapies and diagnosis, model-based identification of metabolic SDLs can be used to model pathogenic bacteria and provide leads to new antibiotic targets.
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